Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images.

ABSTRACT: Breast cancer tumor grade is strongly associated with patient survival. In current clinical practice, pathologists assign tumor grade after visual analysis of tissue specimens. However, different studies show significant inter-observer variation in breast cancer grading. Computer-based breast cancer grading methods have been proposed but only work on specifically selected tissue areas and/or require labor-intensive annotations to be applied to new datasets. In this study, we trained and evaluated a deep learning-based breast cancer grading model that works on whole-slide histopathology images. The model was developed using whole-slide images from 706 young (< 40 years) invasive breast cancer patients with corresponding tumor grade (low/intermediate vs. high), and its constituents nuclea

SUBMITTER: Wetstein SC 

PROVIDER: S-EPMC9448798 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature